What is lead scoring?
Lead scoring is the practice of assigning a numeric or grade-based value to every lead based on fit and behavior, so revenue teams can prioritize who to work next. Fit covers firmographic signals like industry, company size, and job title. Behavior covers engagement signals like page views, email opens, demo requests, and product usage. A good scoring model does two things at once: it ranks leads inside a queue, and it fires routing or nurture workflows when a threshold is crossed. The output is a shared definition of readiness that marketing, sales, and customer success all operate from.
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What is an MQL (marketing qualified lead)?
A marketing qualified lead, or MQL, is a lead whose engagement with marketing content indicates they are likely to become a customer, but who has not yet asked to talk to sales. MQLs usually clear a combined fit and behavior threshold: the right title at the right company plus repeated touches like whitepaper downloads, pricing page visits, or webinar attendance. The MQL stage exists to filter noise out of the sales pipeline and give reps a prioritized list of warm, in-market accounts. Each marketing and sales team defines its own MQL threshold inside its scoring model.
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What is an SQL (sales qualified lead)?
A sales qualified lead, or SQL, is an MQL that a sales rep has accepted after confirming budget, authority, need, and timeline fit the ideal customer profile. The transition from MQL to SQL is the moment a lead moves from the marketing side of the funnel into an active sales opportunity with a dollar value and a close date. Most teams require a discovery call, a disqualification review, or a BANT style checklist before promoting an MQL to SQL. Clear SQL criteria protect sales capacity and prevent reps from working low-intent leads that marketing passed too early.
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What is the difference between an MQL and an SQL?
An MQL is marketing saying this lead looks ready based on engagement, and an SQL is sales confirming that readiness after a conversation. MQLs are scored by fit plus behavior signals from forms, email, and the website. SQLs add a human qualification layer: a rep has validated the pain, the buying process, and the timing. The handoff between the two is the single most audited step in the funnel because misalignment here is where pipeline leaks. Strong scoring programs track MQL to SQL conversion rate weekly and adjust thresholds when the ratio drifts outside the healthy band.
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What is a PQL (product qualified lead)?
A product qualified lead, or PQL, is a user inside a free trial or freemium product who has reached usage milestones that correlate with becoming a paying customer. Common PQL signals include inviting teammates, hitting a feature threshold, importing production data, or crossing a volume limit. PQLs are central to product-led growth because they let sales focus on users who have already proven value in the product, not just visitors who filled out a form. A mature PQL model blends behavioral events with account-level fit so a self-serve signup from a target account gets the right follow-up.
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How do you build a lead scoring model?
Start by defining the ideal customer profile and listing the fit attributes that matter, such as industry, headcount, revenue band, and buyer persona. Then analyze closed-won deals to find the behavioral signals that preceded them, like pricing page visits, demo requests, or multi-threaded engagement. Assign point values to each attribute and signal, set a minimum score for MQL, and add decay rules so stale engagement loses weight over time. Pilot the model on recent cohorts, measure MQL to opportunity conversion, and adjust weights monthly. The model should be a living document owned jointly by marketing operations and revenue operations.
What is a health score, and how is it different from a lead score?
A health score measures the ongoing wellness of an existing customer account, while a lead score measures the readiness of a prospect to buy. Health scores roll up signals like product adoption depth, support ticket trends, executive sponsor engagement, and renewal risk indicators. They power customer success workflows: churn alerts, expansion plays, and executive business reviews. Lead scores stop at the opportunity close, and health scores pick up from there. Many revenue teams run both inside the same CRM, so handoffs between sales and customer success stay inside one record and one shared history.
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What signals should a B2B lead scoring model include?
A durable B2B model combines explicit fit data with implicit behavior data. Fit signals include industry, employee count, revenue range, tech stack, and buyer title. Behavior signals include pricing page views, demo requests, trial activity, webinar attendance, repeat visits, and email engagement. Negative signals matter too: competitor domains, free email addresses, and student titles should subtract points so the queue stays clean. Account level signals, such as multiple contacts from the same company touching the site in a short window, are often the strongest predictors of a real opportunity and belong in every modern model.
How often should you review and update lead scoring?
Review scoring at least once a quarter and whenever a major go-to-market change ships, such as a new product line, a pricing update, or an expansion into a new segment. Monthly, watch MQL to SQL conversion rate, SQL to opportunity rate, and sales acceptance rate for drift. If MQL volume spikes but acceptance drops, the threshold is probably too low. If sales rejects most MQLs, the fit criteria likely need tightening. Scoring is never set and forget: the market moves, the product evolves, and buying committees shift, so the model has to move with them.
What is predictive lead scoring?
Predictive lead scoring uses machine learning to analyze historical won and lost deals and automatically weight the signals that correlate with revenue. Instead of a marketer assigning ten points for a demo request and five for a pricing visit, the model learns those weights from outcomes. Predictive scoring shines when there is enough closed-won volume to train on, usually a few hundred deals at minimum. It is especially helpful for finding non-obvious signals, like a specific job title that converts well in a vertical. The output still needs human review, so sales trusts the ranking and the reasons behind it.
How does lead scoring connect to lead routing?
Lead scoring produces the ranking, and lead routing acts on it. When a lead crosses the MQL threshold, routing rules assign it to the right rep based on territory, segment, product interest, or round-robin logic. The two systems should be joined inside one platform so a score change can trigger an instant assignment, a Slack notification, and a task on the rep calendar without data moving between tools. Fast routing is a huge driver of conversion: leads worked within a few minutes convert at a much higher rate than leads worked the next day, so the scoring and routing loop needs to be measured in seconds, not hours.
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Does Strkr include lead scoring out of the box?
Yes. Strkr ships with configurable lead and account scoring, PQL and health score models, and the automation to turn score changes into routing, nurture, and alerting. Fit rules, behavior signals, decay curves, and MQL thresholds live inside the CRM so marketing, sales, and customer success all see the same number. Scores update in real time as web activity, email engagement, product usage, and manual edits flow in. Teams can start with a template model, tune weights against their closed-won data, and layer in predictive scoring once the deal history is large enough to train on.
